AIMC Topic: Neural Networks, Computer

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Multimodal hybrid convolutional neural network based brain tumor grade classification.

BMC bioinformatics
An abnormal growth or fatty mass of cells in the brain is called a tumor. They can be either healthy (normal) or become cancerous, depending on the structure of their cells. This can result in increased pressure within the cranium, potentially causin...

Reliable interpretability of biology-inspired deep neural networks.

NPJ systems biology and applications
Deep neural networks display impressive performance but suffer from limited interpretability. Biology-inspired deep learning, where the architecture of the computational graph is based on biological knowledge, enables unique interpretability where re...

Predicting individual cases of major adolescent psychiatric conditions with artificial intelligence.

Translational psychiatry
Three-quarters of lifetime mental illness occurs by the age of 24, but relatively little is known about how to robustly identify youth at risk to target intervention efforts known to improve outcomes. Barriers to knowledge have included obtaining rob...

A Method for Predicting Production Costs Based on Data Fusion from Multiple Sources for Industry 4.0: Trends and Applications of Machine Learning Methods.

Computational intelligence and neuroscience
There is a growing need for manufacturing processes that improve product quality and production rates while reducing costs. With the advent of multisensory information fusion technology, individuals can acquire a broader range of information. Several...

Mutual-Attention Net: A Deep Attentional Neural Network for Keyphrase Generation.

Computational intelligence and neuroscience
Neural keyphrase generation (NKG) is a recently proposed approach to automatically extract keyphrase from a document. Unlike the traditional keyphrase extraction, the NKG can generate keyphrases that do not appear in the document. However, as a super...

IoT-Based Reinforcement Learning Using Probabilistic Model for Determining Extensive Exploration through Computational Intelligence for Next-Generation Techniques.

Computational intelligence and neuroscience
Computing intelligence is built on several learning and optimization techniques. Incorporating cutting-edge learning techniques to balance the interaction between exploitation and exploration is therefore an inspiring field, especially when it is com...

Patient Graph Deep Learning to Predict Breast Cancer Molecular Subtype.

IEEE/ACM transactions on computational biology and bioinformatics
Breast cancer is a heterogeneous disease consisting of a diverse set of genomic mutations and clinical characteristics. The molecular subtypes of breast cancer are closely tied to prognosis and therapeutic treatment options. We investigate using deep...

Zero time waste in pre-trained early exit neural networks.

Neural networks : the official journal of the International Neural Network Society
The problem of reducing processing time of large deep learning models is a fundamental challenge in many real-world applications. Early exit methods strive towards this goal by attaching additional Internal Classifiers (ICs) to intermediate layers of...

Sign language recognition using the fusion of image and hand landmarks through multi-headed convolutional neural network.

Scientific reports
Sign Language Recognition is a breakthrough for communication among deaf-mute society and has been a critical research topic for years. Although some of the previous studies have successfully recognized sign language, it requires many costly instrume...

Facilitating cell segmentation with the projection-enhancement network.

Physical biology
Contemporary approaches to instance segmentation in cell science use 2D or 3D convolutional networks depending on the experiment and data structures. However, limitations in microscopy systems or efforts to prevent phototoxicity commonly require reco...